1. Field
The following description relates to an apparatus and method to detect a lesion in a medical image.
2. Description of the Related Art
In the modern medical industry, the use of medical images is indispensable for efficient diagnosis and treatment of a disease. In addition, due to recent advanced development of imaging techniques, it is possible to obtain more accurate and sophisticated medical images.
Less error may occur if a computer is used to diagnose a disease using medical images. Thus, a Computer Aided Diagnosis (CAD) technique is frequently used.
In a CAD system, it is a lesion's morphology, texture, and luminance that play a role in determining whether the lesion is malignant. Thus, lesion detection is an important technique for accurate lesion diagnosis.
Recently, numerous techniques to automatically detect a lesion using a computer have been developed. For example, a graph cut is a technique to detect a lesion by labelling each pixel or small-size region (known as superpixel) of an image.
In addition, an active contour model and a level set method have been used to detect a contour of a lesion.
In such lesion detection methods, a lesion is detected by segmenting the lesion and determining a contour thereof where an energy function has a minimal solution. That is, a value of each parameter in the energy function needs to be optimized.
Generally, in the lesion detection methods, a user selects a proper value for each parameter heuristically or based on domain knowledge, and uses the proper value as a fixed value.
However, accurate lesion detection is challenging in the case of an ultrasound mammogram image and any other image of which qualities vary according to an image capturing device, expertise of a doctor, a breast density, an intensity of an ultrasound transducer, and a surrounding environment.